Hierarchical Reinforcement Learning (HRL)

An advanced method where AI agents break down complex tasks into smaller, manageable sub-tasks, learning to solve each one hierarchically.

This structure allows agents to tackle large-scale problems efficiently, such as robotic manipulation or multi-step decision-making, by mastering each sub-task and combining them to achieve the overall goal.